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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Introduction to Data Mining- Benefits, Techniques and Applications

Amruta
Amruta Last Updated : 01 Mar, 2024
7 min read

Introduction

Data mining, a crucial aspect of the data science realm, involves uncovering hidden insights and patterns within datasets to extract valuable information. This article serves as a comprehensive guide to understanding the fundamental concepts, tasks, applications, and tools associated with data mining. From basic data mining tasks like classification and prediction to exploring advanced algorithms and real-life applications, this overview aims to provide insights into the diverse facets of data mining.

This article was published as a part of the Data Science Blogathon.

Table of contents

What is Data Mining

“Data Mining”,  that mines the data. In simple words, it is defined as finding hidden insights(information) from the database, extract patterns from the data.

There are different algorithms for different tasks. The function of these algorithms is to fit the model. These algorithms identify the characteristics of data. There are 2 types of models.

Predictive model

A predictive model is a statistical or machine learning model that is designed to predict future outcomes or behaviors based on historical data. These models analyze patterns and relationships within the data to make predictions about unseen or future instances. Predictive models are commonly used in various fields such as finance, marketing, healthcare, and weather forecasting. They aim to forecast trends, identify potential risks, or make informed decisions based on the insights gained from the data.

Descriptive model

A descriptive model is used to summarize and describe the characteristics of a dataset. Unlike predictive models, descriptive models focus on understanding and explaining the existing data rather than making predictions about future outcomes. These models aim to uncover patterns, relationships, and insights within the data to gain a better understanding of the underlying structure or behavior. Descriptive models are often used for exploratory data analysis, reporting, and visualization purposes to communicate insights to stakeholders effectively.

What is Data Mining

Benefits of Data Mining

Key benefits of data mining:

  • Improved Decision Making: Data mining helps businesses make informed decisions by providing valuable insights and predictions. For example, a retail store can use data mining to understand which products are often bought together and use this information to optimize product placement.
  • Enhanced Customer Engagement: By understanding customer behavior and preferences, businesses can tailor their services to meet customer needs, leading to improved customer satisfaction and loyalty.
  • Efficient Operations: Data mining can help identify inefficiencies in a business process. For instance, a manufacturing company can use data mining to detect bottlenecks in their production process and take corrective action.
  • Risk Management: Data mining can help businesses identify potential risks and take preventive measures. For example, banks can use data mining to predict the likelihood of a customer defaulting on a loan.
  • New Opportunities: Data mining can uncover hidden patterns and trends that can lead to new business opportunities. For instance, a streaming service can use data mining to identify popular genres and invest in creating content in those genres.

Data Mining Tools

Data miners need tools to extract, transform, and analyze data. Here are some popular data mining tools:

  • RapidMiner: Think of RapidMiner as a Swiss Army knife for data mining. It’s a versatile tool that can handle data preprocessing, machine learning, and predictive modeling. It’s user-friendly with a drag-and-drop interface, making it a great choice for beginners.
  • Weka: Weka is like the old reliable friend in the world of data mining. It’s been around for a while and has a comprehensive collection of data preprocessing and modeling techniques. It’s open-source and easy to use, making it popular in academia and industry alike.
  • Orange: Orange is a bit like a box of Lego blocks – it allows you to visually build data workflows. It’s great for exploratory data analysis and has a range of machine learning algorithms. Plus, it’s open-source!
  • KNIME: KNIME is like a high-tech conveyor belt for your data. It allows you to visually design data workflows and has a vast array of features for data blending, machine learning, and reporting.
  • Python and R: Python and R are like the superheroes of data mining. They are programming languages that have powerful libraries (like pandas, scikit-learn, ggplot) for data mining tasks. They are highly flexible and powerful, but require programming knowledge.

Basic Data Mining Tasks

Under this section, we are going to see some of the mining functions/tasks.

Classification

This term comes under supervised learning. Classification algorithms require that the classes should be defined based on variables. Characteristics of data define which class belongs to. Pattern recognition is one of the types of classification problems in which input(pattern) is classified into different classes based on its similarity of defined classes.

Prediction

In real life, we often see predicting future things/values/or else based on past data and present data. Prediction is also a type of classification task. According to the type of application, for example, predicting flood where dependant variables are the water level of the river, its humidity, raining scale, and so on are the attributes.

Regression

Data Mining regression

Regression is a statistical technique that is used to determine the relationship between variables(x) and dependant variables(y). There are few types of regression as Linear, Logistic, etc. Linear Regression is used in continuous values(0,1,1.5,….so on) and Logistic Regression is used where there is the possibility of only two events such as pass/fail, true/false, yes/no, etc.

Time Series Analysis

Data Mining time series

In time series analysis, a variable changes its value according to time. It means analysis goes under the identifying patterns of data over a period of time. It can be seasonal variation, irregular variation, secular trend, and cyclical fluctuation. For example, annual rainfall, stock market price, etc.

Clustering

clustering

Clustering is the same as classification i.e it groups the data. Clustering comes under unsupervised machine learning. It is a process of partitioning the data into groups based on similar kinds of data.

Summarization

Summarization is nothing but characterization or generalization. It retrieves meaningful information from data. It also gives a summary of numeric variables such as mean, mode, median, etc.

Association Rules

It’s the main task of Mining. It helps in finding appropriate patterns and meaningful insights from the database. Association Rule is a model which extracts types of data associations. For example, Market Basket Analysis where association rules are applied to the database to know that which items are purchased together by the customer.

Association rules Data Mining

 

Sequence Discovery

It is also called sequential analysis. It is used to discover or find the sequential pattern in data.

Sequential Pattern means the pattern which is purely based on a sequence of time. These patterns are similar to found association rules in database or events are related but its relationship is based only on “Time”.

Up to this point, we have seen all the basic functions or tasks of Mining. Let’s go-ahead to know more about Data Mining…

Data Mining VS KDD(Knowledge Discovery in Database)

Data Mining: Process of use of algorithms to extract meaningful information and patterns derived from the KDD process. It is a step involved in KDD.

KDD: It is a significant process of identifying meaningful information and patterns in Data. The input is given to this process is data and output gives useful information from data.

KDD process consists 5 steps:

1)Selection: Need to obtain data from various data sources, databases.

2)Preprocessing: This process of cleaning data in terms of any incorrect data, missing values, erroneous data.

3)Transformation: Data from various sources must be converted, encoded into some format for preprocessing.

4)Data Mining: In this process, algorithms are applied to transformed data to achieve desired output/results.

5)Interpretation/evaluation: Has to perform some visualizations to present data mining results which are very important.

kdd data mining

Data Mining Applications

E-Commerce 

E-commerce is one of the real-life applications of it. E-commerce companies are like Amazon, Flipkart, Myntra, etc. They use mining techniques to see the functionality of every product in such a way that “which product is viewed most by the customer also what they also liked other”.

Retailing

It is another application from the retail market. Retailers find the pattern of “Freshness, Frequency, Monetary(In terms of Currency)”.  Retailers keep the track of sales of products, transactions.

Education

Education is an emerging, trending field nowadays. It concerns knowledge discovery from educational data. The main goal of this application is to study or identify the student’s behavior pattern in terms of future learning, effects of study, advanced knowledge of learning, etc. These data mining techniques are used by institutions to take accurate decisions and also predict appropriate results.

Data Mining Algorithms

  •  K-means clustering
  • Support vector machines
  • Apriori
  •  KNN
  • Naive Bayes
  • CART and many more…

These are few algorithms.

Now I am going to give you information about the required libraries below.

Apriori:

from apyori import apriori

K-means clustering:

from kneed import KneeLocator
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler

Support Vector Machines:

from sklearn import svm

Naive Bayes:

from sklearn.naive_bayes import GaussianNB

CART:

from sklearn.tree import DecisionTreeRegressor

KNN:

from
 
sklearn.neighbors 
import
 
KNeighborsClassifier

So here are few libraries that to be installed while performing the algorithm.

Conclusion

Data mining plays a pivotal role in modern data-driven decision-making processes across various industries. By leveraging predictive and descriptive models, enables organizations to derive actionable insights from vast amounts of data, driving strategic decision-making and enhancing operational efficiency. With a basic understanding of Python and database concepts, individuals can explore the vast landscape of data mining, employing algorithms and tools to extract valuable knowledge from datasets. As data continues to proliferate, remains a vital tool for unlocking the hidden potential within data, empowering organizations to gain a competitive edge in today’s data-driven world.

Frequently Asked Questions

Q1. What is the difference between data mining and machine learning?

A. Data mining primarily focuses on extracting patterns and insights from existing datasets, often using statistical techniques and algorithms. Machine learning, on the other hand, involves the development of algorithms that enable computers to learn from data and make predictions or decisions without being explicitly programmed.

Q2. What are some popular tools used for data mining?

A. Some popular tools for data mining include KNIME, WEKA, and ORANGE. These tools provide a user-friendly interface and a wide range of functionalities for tasks such as data preprocessing, modeling, and visualization, making them valuable assets for data analysts and scientists.

Q3. How can data mining be applied in real-world scenarios?

A.Finds applications in various domains such as e-commerce, retailing, healthcare, and education. For example, e-commerce companies utilize data mining techniques to analyze customer behavior, recommend products, and optimize marketing strategies. In healthcare, data mining helps identify patterns in patient data for disease diagnosis and treatment planning. These real-world applications highlight the versatility and importance of data mining in today’s data-driven society.

Amruta
Amruta

I am Software Engineer, data enthusiast , passionate about data and its potential to drive insights, solve problems and also seeking to learn more about machine learning, artificial intelligence fields.

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